Project-Template for IW276 Autonome Systeme Labor

Short introduction to project assigment.

Screenshot / GIF
Link to Demo Video

This work was done by Jonashar, Mack0438, and subpathdev during the IW276 Autonome Systeme Labor at the Karlsruhe University of Applied Sciences (Hochschule Karlruhe - Technik und Wirtschaft) in SS 2021.

Table of Contents

• Requirements
• Prerequisites
• Pre-trained model
• Running
• Acknowledgments

Requirements

• Python 3.6 (or above)
• OpenCV 4.1 (or above)
• Jetson Nano
• Jetpack 4.4

[Optional] ...

Prerequisites

1. Install requirements:

pip install -r requirements.txt

Pre-trained models

Pre-trained model is available at pretrained-models/

Running

To run the demo, pass path to the pre-trained checkpoint and camera id (or path to video file):

python src/applyModel.py

Docker

We are using a base image which includes only the requirements. To build that image you can execute use docker build -t docker.pkg.github.com/iw276/iw276ss21-p16/base_image -f base_image.dockerfile. You can download the already created container too currently you need the read packages permission do do this. If the base image can be found on your local system you can build the app container with the following command docker build -t docker.pkg.github.com/iw276/iw276ss21-p16/app -f app.dockerfile.

To start the container you can use the following command where the directory datasets contains all tested files.

mkdir out
docker run --runtime=nvidia -v $(realpath datasets):/app/testdata -v $(realpath out):/app/out docker.pkg.github.com/iw276/iw276ss21-p16/app:latest

We are assumed that the tag latest exists and references to the latest built container image.

Acknowledgments

This repo is based on

• Envirnmental-Sound-Classification

Thanks to the original authors for their work!

Contact

Please email mickael.cormier AT iosb.fraunhofer.de for further questions.